{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f645787bac8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MHJLio5lyek+KSwwzFmwJtkiKojRxVFmEIOmZuaS0iSM5MY6z+7Zn5qKt5ObrrpiKogQP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\ncWPhOI7jZMWNheM4jpMVNxaO4zhOVv4PGY3kOvHgPKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f646873ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X, y = load_breast_cancer(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=11)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "nb = GaussianNB()\n",
    "\n",
    "lr_scores = []\n",
    "nb_scores = []\n",
    "\n",
    "train_sizes = range(10, len(X_train), 10)\n",
    "\n",
    "for train_size in train_sizes:\n",
    "    X_slice, _, y_slice, _ = train_test_split(\n",
    "        X_train, y_train, train_size=train_size, stratify=y_train, random_state=11)\n",
    "    nb.fit(X_slice, y_slice)\n",
    "    nb_scores.append(nb.score(X_test, y_test))\n",
    "    lr.fit(X_slice, y_slice)\n",
    "    lr_scores.append(lr.score(X_test, y_test))\n",
    "    \n",
    "plt.plot(train_sizes, nb_scores, label='Naive Bayes')\n",
    "plt.plot(train_sizes, lr_scores, linestyle='--', label='Logistic Regression')\n",
    "plt.title(\"Naive Bayes and Logistic Regression Accuracies\")\n",
    "plt.xlabel(\"Number of training instances\")\n",
    "plt.ylabel(\"Test set accuracy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f64576dd588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sghI+X3+Ai0dH19uvclwYAwsfgnZh+N3yHXuiJnsMLLgnLY9ZS3cxbWR3zhjY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\nSqGosVAURVEKRY2FoiiKUij/D56KsUof9GTVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64577a8ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('./pima-indians-diabetes.data', header=None)\n",
    "y = df[8]\n",
    "X = df[[0, 1, 2, 3, 4, 5, 6, 7]]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=11)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "nb = GaussianNB()\n",
    "\n",
    "lr_scores = []\n",
    "nb_scores = []\n",
    "\n",
    "train_sizes = range(10, len(X_train), 10)\n",
    "\n",
    "for train_size in train_sizes:\n",
    "    X_slice, _, y_slice, _ = train_test_split(\n",
    "        X_train, y_train, train_size=train_size, stratify=y_train, random_state=11)\n",
    "    nb.fit(X_slice, y_slice)\n",
    "    nb_scores.append(nb.score(X_test, y_test))\n",
    "    lr.fit(X_slice, y_slice)\n",
    "    lr_scores.append(lr.score(X_test, y_test))\n",
    "    \n",
    "plt.plot(train_sizes, nb_scores, label='Naive Bayes')\n",
    "plt.plot(train_sizes, lr_scores, linestyle='--', label='Logistic Regression')\n",
    "plt.title(\"Naive Bayes and Logistic Regression Accuracies\")\n",
    "plt.xlabel(\"Number of training instances\")\n",
    "plt.ylabel(\"Test set accuracy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
